{"id":"W3003430042","doi":"10.1016/j.automatica.2020.108834","title":"A unitary distributed subgradient method for multi-agent optimization with different coupling sources","year":2020,"lang":"en","type":"article","venue":"Automatica","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subgradient method; Unitary state; Coupling (piping); Multi-agent system; Mathematical optimization; Computer science; Mathematics; Distributed computing; Artificial intelligence; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000268429,0.000317709,0.0004701451,0.00006994657,0.0002230344,0.0002761545,0.0008396001,0.00007815463,0.000010532],"category_scores_gemma":[0.0001923277,0.0002447923,0.0001242395,0.0004643605,0.00003530847,0.0002804674,0.0001691148,0.0001187035,0.00001537447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088011,"about_ca_system_score_gemma":0.00006525933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002384816,"about_ca_topic_score_gemma":0.000007016171,"domain_scores_codex":[0.9978867,0.0001042568,0.0005130459,0.0006059019,0.0004047081,0.0004853447],"domain_scores_gemma":[0.998387,0.0003170179,0.0002688677,0.0005164078,0.0001834299,0.0003272586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008730429,0.000503443,0.001412971,0.0003964166,0.0004709194,0.00003788282,0.003715899,0.9767967,0.0007178743,0.01131559,0.001013915,0.00353101],"study_design_scores_gemma":[0.001972183,0.00025317,0.001126077,0.00007099145,0.00007745445,0.000007058214,0.000152128,0.9947464,0.0006186658,0.00003011361,0.0006289973,0.0003167295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008763111,0.0001038837,0.9858719,0.002882573,0.0001569272,0.001252388,0.000127476,0.0008297205,0.00001207339],"genre_scores_gemma":[0.5357819,0.000002443977,0.4634015,0.0003016075,0.00005094213,0.0002354032,0.0001873443,0.0000257939,0.00001314246],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5270188,"threshold_uncertainty_score":0.9982342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03773203423915227,"score_gpt":0.2709853742062979,"score_spread":0.2332533399671456,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}